Causlist                This file contains all the classes needed for
                        the PSOHO structure learning algorithm. It was
                        implemented as an independent package in
                        https://github.com/dkesada/PSOHO and then
                        merged into dbnR. All the original source files
                        are merged into one to avoid bloating the R/
                        folder of the package.
Particle                R6 class that defines a Particle in the PSO
                        algorithm
Position                R6 class that defines DBNs as causality lists
PsoCtrl                 R6 class that defines the PSO controller
Velocity                R6 class that defines velocities affecting
                        causality lists in the PSO
acc_successions         Returns a vector with the number of consecutive
                        nodes in each level
add_attr_to_fit         Adds the mu vector and sigma matrix as
                        attributes to the bn.fit or dbn.fit object
approx_prediction_step
                        Performs approximate inference in a time slice
                        of the dbn
approximate_inference   Performs approximate inference forecasting with
                        the GDBN over a data set
as_named_vector         Converts a single row data.table into a named
                        vector
bn_translate_exp        Experimental function that translates a
                        natPosition vector into a DBN network.
calc_mu                 Calculate the mu vector of means of a Gaussian
                        linear network. Front end of a C++ function.
calc_mu_cpp             Calculate the mu vector of means of a Gaussian
                        linear network. This is the C++ backend of the
                        function.
calc_sigma              Calculate the sigma covariance matrix of a
                        Gaussian linear network. Front end of a C++
                        function.
calc_sigma_cpp          Calculate the sigma covariance matrix of a
                        Gaussian linear network. This is the C++
                        backend of the function.
check_time0_formatted   Checks if the vector of names are time
                        formatted to t0
cl_to_arc_matrix_cpp    Create a matrix with the arcs defined in a
                        causlist object
create_blacklist        Creates the blacklist of arcs from a folded
                        data.table
create_causlist_cpp     Create a causal list from a DBN. This is the
                        C++ backend of the function.
create_natcauslist_cpp
                        Create a natural causal list from a DBN. This
                        is the C++ backend of the function.
crop_names_cpp          If the names of the nodes have "_t_0" appended
                        at the end, remove it
cte_times_vel_cpp       Multiply a Velocity by a constant real number
dmmhc                   Learns the structure of a markovian n DBN model
                        from data
dynamic_ordering        Gets the ordering of a single time slice in a
                        DBN
exact_inference         Performs exact inference forecasting with the
                        GDBN over a data set
exact_inference_backwards
                        Performs exact inference smoothing with the
                        GDBN over a data set
exact_prediction_step   Performs exact inference in a time slice of the
                        dbn
expand_time_nodes       Extends the names of the nodes in t_0 to
                        t_(max-1)
filter_same_cycle       Filter the instances in a data.table that have
                        values of different ids in each row
filtered_fold_dt        Fold a dataset to a certain size and avoid
                        overlapping of different time-series
fit_dbn_params          Fits a markovian n DBN model
fold_dt                 Widens the dataset to take into account the t
                        previous time slices
fold_dt_rec             Widens the dataset to take into account the t
                        previous time slices
forecast_ts             Performs forecasting with the GDBN over a data
                        set
generate_random_network_exp
                        Experimental function that generates a random
                        DBN and samples a dataset that defines it
init_cl_cpp             Initialize the nodes vector
init_list_cpp           Initialize the particles
initialize_cl_cpp       Create a causality list and initialize it
learn_dbn_struc         Learns the structure of a markovian n DBN model
                        from data
merge_nets              Merges and replicates the arcs in the static BN
                        into all the time-slices in the DBN
motor                   Multivariate time series dataset on the
                        temperature of an electric motor
mvn_inference           Performs inference over a multivariate normal
                        distribution
natCauslist             This file contains all the classes needed for
                        the natPSOHO structure learning algorithm. It
                        was implemented as an independent package in
                        https://github.com/dkesada/natPSOHO and then
                        merged into dbnR. All the original source files
                        are merged into one to avoid bloating the R/
                        folder of the package.
natParticle             R6 class that defines a Particle in the PSO
                        algorithm
natPosition             R6 class that defines DBNs as vectors of
                        natural numbers
natPsoCtrl              R6 class that defines the PSO controller
natPsoho                Learn a DBN structure with a PSO approach
natVelocity             R6 class that defines velocities in the PSO
nat_cl_to_arc_matrix_cpp
                        Create a matrix with the arcs defined in a
                        causlist object
nat_cte_times_vel_cpp   Multiply a Velocity by a constant real number
nat_pos_minus_pos_cpp   Subtracts two natPositions to obtain the
                        natVelocity that transforms ps1 into ps2
nat_pos_plus_vel_cpp    Add a velocity to a position
nat_vel_plus_vel_cpp    Adds two natVelocities
node_levels             Defines a level for every node in the net
nodes_gen_exp           Generates the names of the nodes in t_0 and in
                        all the network
one_hot                 One hot encoder for natural numbers without the
                        0.
one_hot_cpp             One-hot encoder for natural numbers without the
                        0
ordering_gen_exp        Generates the names of n variables.
plot_dynamic_network    Plots a dynamic Bayesian network in a
                        hierarchical way
plot_network            Plots a Bayesian networks or a dynamic Bayesian
                        network
plot_static_network     Plots a Bayesian networks in a hierarchical way
pos_minus_pos_cpp       Subtracts two Positions to obtain the Velocity
                        that transforms one into the other
pos_plus_vel_cpp        Add a velocity to a position
predict_bn              Performs inference over a fitted GBN
predict_dt              Performs inference over a test data set with a
                        GBN
psoho                   Learn a DBN structure with a PSO approach
randomize_vl_cpp        Randomize a velocity with the given
                        probabilities
recount_arcs_exp        Experimental function that recounts the number
                        of arcs in the position
reduce_freq             Reduce the frequency of the time series data in
                        a data.table
rename_nodes_cpp        Return a list of nodes with the time slice
                        appended up to the desired size of the network
smooth_ts               Performs smoothing with the GDBN over a data
                        set
time_rename             Renames the columns in a data.table so that
                        they end in '_t_0'
trunc_geom              Geometric distribution sampler truncated to a
                        maximum
vel_plus_vel_cpp        Add two Velocities
